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Abiodun Musa Aibinu
Researcher at Federal University of Technology Minna
Publications - 112
Citations - 1023
Abiodun Musa Aibinu is an academic researcher from Federal University of Technology Minna. The author has contributed to research in topics: Artificial neural network & Autoregressive model. The author has an hindex of 16, co-authored 101 publications receiving 735 citations. Previous affiliations of Abiodun Musa Aibinu include International Islamic University Malaysia.
Papers
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Journal ArticleDOI
Vascular intersection detection in retina fundus images using a new hybrid approach.
Abiodun Musa Aibinu,Muhammad Imran Iqbal,Amir Akramin Shafie,Momoh Jimoh Emiyoka Salami,Mikael Nilsson +4 more
TL;DR: The proposed combined cross-point number (CCN) method has a very high precision, accuracy, sensitivity and low false rate in detecting both bifurcation and crossover points in FIs compared with both the MCN and the SCN methods.
Journal ArticleDOI
An optimized routing algorithm for vehicle ad-hoc networks
TL;DR: This paper presents a twofold approach entailing the design of a new route metric for VANET communication, which considers important parameters such as the received signal strength; transmit power, frequency and the path loss, and suggests that IGAROT improves road anomaly communication among vehicles.
Journal ArticleDOI
A novel Clustering based Genetic Algorithm for route optimization
TL;DR: Cl clustering based GA with polygamy and dynamic population control mechanism have been proposed and showed that the proposed algorithm outperforms some of the existing techniques and converged to global solution within few iterations (generations) thus favoring its acceptability for online-realtime applications.
Journal ArticleDOI
New road anomaly detection and characterization algorithm for autonomous vehicles
TL;DR: A new algorithm for detecting and characterizing potholes and bumps directly from noisy signals acquired using an Accelerometer is presented, showing high levels of accuracy, precision and low false alarm rates.
Proceedings ArticleDOI
Image processing techniques for automated road defect detection: A survey
TL;DR: The aim of this paper is to survey existing works, with emphasis on pothole road defect detection using Image processing techniques, and the strengths and limitations of these image processing methods are highlighted with specific areas for improvement identified.